Fighting Uncertainty in Highly Dynamic Wireless Sensor Networks with Probabilistic Models

Real-time operation in Wireless Sensor Networks (WSNs) is conditioned not only by the current technological level (e.g., limited computing power) but also inherently by the target problem itself: WSNs are required to operate in very open and uncertain environments, subject to external radio interferences, highly dynamic network load, etc. Current WSN solutions either provide only best-effort real-time guarantees or make (generally implicit) assumptions on the dynamics of the open environment. These assumptions, in turn, are either very relaxed (i.e., compatible only with undemanding real-time requirements) or very hard to justify. When dealing with WSNs supporting highly dynamic applications and operating environments (e.g., media streaming, robot control, vehicle coordination, etc.) this problem cannot be ignored. Accordingly, we argue for, and show the efficacy of, using probabilistic models to characterize dynamic WSN QoS, which is the first step to tackle the problem head on. Using our network monitoring technique, we demonstrate that it is possible to meet probabilistic real-time objectives.